Segmoe 4x2 V0

🚀 SegMoE-4x2-v0：Segmind扩散专家混合模型

SegMoE-4x2-v0是一个未经训练的Segmind扩散专家混合模型，它使用segmoe框架，基于4个专家SDXL模型生成。SegMoE是一个强大的框架，可在几分钟内将多个稳定扩散模型动态组合成专家混合模型，无需进行训练。该框架允许即时创建更大的模型，这些模型具有更广泛的知识、更好的贴合度和更高的图像质量。

🚀 快速开始

本模型可通过segmoe库使用。

📦 安装指南

请确保通过以下命令安装segmoe：

pip install segmoe

💻 使用示例

基础用法

from segmoe import SegMoEPipeline

pipeline = SegMoEPipeline("segmind/SegMoE-4x2-v0", device = "cuda")

prompt = "cosmic canvas, orange city background, painting of a chubby cat"
negative_prompt = "nsfw, bad quality, worse quality"
img = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=1024,
    width=1024,
    num_inference_steps=25,
    guidance_scale=7.5,
).images[0]
img.save("image.png")

🔧 技术细节

Config

用于创建此模型的配置如下：

base_model: SG161222/RealVisXL_V3.0
num_experts: 4
moe_layers: all
num_experts_per_tok: 2
experts:
  - source_model: frankjoshua/juggernautXL_v8Rundiffusion
    positive_prompt: "aesthetic, cinematic, hands, portrait, photo, illustration, 8K, hyperdetailed, origami, man, woman, supercar"
    negative_prompt: "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
  - source_model: SG161222/RealVisXL_V3.0
    positive_prompt: "cinematic, portrait, photograph, instagram, fashion, movie, macro shot, 8K, RAW, hyperrealistic, ultra realistic,"
    negative_prompt: "(octane render, render, drawing, anime, bad photo, bad photography:1.3), (worst quality, low quality, blurry:1.2), (bad teeth, deformed teeth, deformed lips), (bad anatomy, bad proportions:1.1), (deformed iris, deformed pupils), (deformed eyes, bad eyes), (deformed face, ugly face, bad face), (deformed hands, bad hands, fused fingers), morbid, mutilated, mutation, disfigured"
  - source_model: albertushka/albertushka_DynaVisionXL
    positive_prompt: "minimalist, illustration, award winning art, painting, impressionist, comic, colors, sketch, pencil drawing,"
    negative_prompt: "Compression artifacts, bad art, worst quality, low quality, plastic, fake, bad limbs, conjoined, featureless, bad features, incorrect objects, watermark, ((signature):1.25), logo"
  - source_model: frankjoshua/albedobaseXL_v13
    positive_prompt: "photograph f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K"
    negative_prompt: "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, blurry"

其他变体

我们在Hugging Face上发布了3个合并模型：

• SegMoE 2x1 包含两个专家模型。
• SegMoE SD 4x2 包含四个稳定扩散1.5专家模型。

对比

从以下图像可以看出，提示理解能力有所提高。从左到右依次为SegMoE-2x1-v0、SegMoE-4x2-v0、基础模型（RealVisXL_V3.0）

模型描述

适用范围外使用

SegMoE-4x2-v0模型不适用于创建人物、事件或现实世界信息的事实性或准确表示。它不用于需要高精度和准确性的任务。

✨ 主要特性

• 多专家知识融合：受益于多个微调专家的知识。
• 无需训练：无需进行训练，即可快速组合模型。
• 更好的数据适应性：能更好地适应数据。
• 模型可升级：可以通过使用更好的微调模型作为专家之一来升级模型。

局限性

• 依赖专家知识：虽然该模型在图像保真度和贴合度上有所改进，但在未训练的情况下，它不会比任何一个专家模型有显著提升，并且依赖于专家的知识。
• 速度未优化：目前尚未针对速度进行优化。
• 内存使用未优化：该框架尚未针对内存使用进行优化。

📄 许可证

本模型采用Apache 2.0许可证。

引用

@misc{segmoe,
  author = {Yatharth Gupta, Vishnu V Jaddipal, Harish Prabhala},
  title = {SegMoE},
  year = {2024},
  publisher = {HuggingFace},
  journal = {HuggingFace Models},
  howpublished = {\url{https://huggingface.co/segmind/SegMoE-4x2-v0}}
}